Automated driving functions require an accurate environmental and navigational map. Beyond the course of roads, which may already be sufficient for a navigation, such a map must also provide highly detailed and current data, which are required for guiding the motor vehicle, for instance, lane information, traffic regulations, current obstacles such as construction sites, and others. These data have to be able to be assigned at high geometric resolution of their positions on a road. Such map data are maintenance-intensive, require large memories for their storage and great bandwidths for their distribution. It is customary to keep in reserve directly only a part of these data onboard a motor vehicle and to request other data only when needed. The data, in this context, are usually exchanged in a wireless mode between the motor vehicle and surroundings or between vehicles at close range. So far, it is not clear how sufficiently great map coverage can be achieved in order to enable large-area, automated driving.
For the navigation of a motor vehicle, it is known that one should drive along a predetermined route in a so-called teach-in mode, in order to be able to use the route advantageously in later automated route navigation. A commuter, who drives the same route daily, may preferentially use a certain variant of the road routing, for example.